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In this paper, we aim to provide an extensive analysis to understand how various factors influence electricity prices in competitive markets, focusing on the day-ahead electricity market in Romania.

Our study period began in January 2019, before the COVID-19 pandemic, and continued for several months after the onset of the war in Ukraine. During this time, we also consider other challenges like reduced market competitiveness, droughts and water scarcity. Our initial dataset comprises diverse variables: prices of essential energy sources (like gas and oil), Danube River water levels (indicating hydrological conditions), economic indicators (such as inflation and interest rates), total energy consumption and production in Romania and a breakdown of energy generation by source (coal, gas, hydro, oil, nuclear and renewable energy sources) from various data sources. Additionally, we included carbon certificate prices and data on electricity import, export and other related variables. This dataset was collected via application programming interface (API) and web scraping, and then synchronized by date and hour.

We discover that the competitiveness significantly affected electricity prices in Romania. Furthermore, our study of electricity price trends and their determinants revealed indicators of economic health in 2019 and 2020. However, from 2021 onwards, signs of a potential economic crisis began to emerge, characterized by changes in the normal relationships between prices and quantities, among other factors. Thus, our analysis suggests that electricity prices could serve as a predictive index for economic crises. Overall, the Granger causality findings from 2019 to 2022 offer valuable insights into the factors driving energy market dynamics in Romania, highlighting the importance of economic policies, fuel costs and environmental regulations in shaping these dynamics.

We combine principal component analysis (PCA) to reduce the dataset’s dimensionality. Following this, we use continuous wavelet transform (CWT) to explore frequency-domain relationships between electricity price and quantity in the day-ahead market (DAM) and the components derived from PCA. Our research also delves into the competitiveness level in the DAM from January 2019 to August 2022, analyzing the Herfindahl-Hirschman index (HHI).

The main purpose of this study is to analyze the evolution of economic and environmental factors in Finland during the period 1990–2022, exploring the complex interactions between Gross Domestic Product (GDP), nuclear energy production, innovation (measured by patents) and the electric grid load capacity factor (LCF).

To achieve the stated purpose, econometric models such as Autoregressive Distributed Lag and cointegration tests were employed to investigate relationships and trends in the available economic and energy data for Finland. For conducting the proposed analyses, EViews was used for econometric approaches, and the Python language was utilized for constructing the Environmental Kuznets Curve.

Following the conducted analyses, several relevant findings have been observed: 1) a complex relationship between GDP and LCF has been identified, noting a long-term decrease in the electricity grid LCF with GDP growth. This result emphasizes the importance of strategic planning in energy policy to maintain stability and efficiency of the grid amidst economic growth; 2) nuclear energy and innovation have shown a mixed impact on LCF, with both positive and negative effects. This finding highlights the necessity to develop policies that encourage the progressive integration of new technologies to minimize the negative impact on electricity grid efficiency; and 3) to maximize the efficient use of the energy system’s capacity, policymakers should aim to balance economic growth with responsible management of energy resources. The integration of renewable energies and continuous investments in research and development are essential for ensuring a sustainable energy transition in Finland.

The study makes a significant contribution by identifying and analyzing in detail the interdependencies between economic growth, innovation and energy sustainability in Finland, providing new perspectives for the development of public policies and economic strategies in the current context of global climate change and energy transition.

The COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of geopolitical risk (GPR) and other indices on TA over 1995–2023.

We employ a nonlinear autoregressive distributed lag (NARDL) model to analyze the effects, capturing both the positive and negative shocks of these variables on TA.

Our research demonstrates that the NARDL model is more effective in elucidating the complex dynamics between macroeconomic factors and TA. Both an increase and a decrease in GPR lead to an increase in TA. A 1% negative shock in GPR leads to an increase in TA by 1.68%, whereas a 1% positive shock in GPR also leads to an increase in TA by 0.5%. In other words, despite the increase in GPR, the number of tourists coming to Russia increases by 0.5% for every 1% increase in that risk. Several explanations could account for this phenomenon: (1) risk-tolerant tourists: some tourists might be less sensitive to GPR or they might find the associated risks acceptable; (2) economic incentives: increased risk might lead to a depreciation in the local currency and lower costs, making travel to Russia more affordable for international tourists; (3) niche tourism: some tourists might be attracted to destinations experiencing turmoil, either for the thrill or to gain firsthand experience of the situation; (4) lagged effects: there might be a time lag between the increase in risk and the actual impact on tourist behavior, meaning the effects might be observed differently over a longer period.

Our study, employing the NARDL model and utilizing a dataset spanning from 1995 to 2023, investigates the impact of GPR, gross domestic product (GDP), real effective exchange rate (REER) and economic policy uncertainty (EPU) on TA in Russia. This research is unique because the dataset was compiled by the authors. The results show a complex relationship between GPR and TA, indicating that factors influencing TA can be multifaceted and not always intuitive.

The Black Sea countries hold geopolitical, economic and environmental relevance. This paper aims to analyze the relationship between CO₂ emissions, economic growth, urbanization (URB), access to electricity (ACEL), foreign direct investment (FDI) and the integration of renewables (RES) in the region, offering insights from both economic and environmental perspectives.

The paper conducts an econometric analysis using the autoregressive distributed lag (ARDL) model to examine the long-term and short-term relationships between GDP, CO2 emissions, FDI and RES integration in the Black Sea region.

The results indicate a positive long-term relationship between GDP and CO₂ emissions, with a negative coefficient for GDP squared, supporting the environmental Kuznets curve (EKC) hypothesis. FDI is found to reduce CO₂ emissions in the long term, rejecting the pollution haven hypothesis. In the short term, the EKC shows a less distinct and more volatile inverted U-shape relationship between GDP and CO₂ emissions. The error correction term (ECT) is negative and statistically significant, suggesting a 75% correction rate when CO₂ emissions deviate from the long-run equilibrium.

This study provides novel insights by linking economic growth, RES integration, FDI and environmental impact in the Black Sea region. It challenges the pollution haven hypothesis and offers nuanced perspectives on the short-term and long-term environmental impacts of economic activities.